volume | PIER Journals

Abstract

Aircraft target detection in synthetic aperture radar (SAR) images faces numerous challenges, primarily including weak contrast, diverse morphologies, and faint signals, which are even more pronounced in complex backgrounds. Meanwhile, practical deployment environments are constrained by limited computational resources and energy consumption, making it essential to balance detection accuracy with model lightweight design. To address this, this paper proposes a lightweight detection network that integrates multi-branch feature enhancement. First, a Parallel Aggregation and Calibration (PAC) module is designed to achieve collaborative modeling of local and global information through multi-scale dilated convolutions; second, a Moment Channel Attention (MCA) module based on higher-order statistical features is introduced to enhance the model's sensitivity to weak signals and target boundaries; finally, during the network fusion stage, the branch calibration connections in the PAC module are removed, and a frequency-domain-driven Efficient Discriminative Frequency domain-based FFN (EDFFN) module is incorporated to improve detailed representation of low-contrast and blurred targets. Experimental results on the SAR-Aircraft-1.0 dataset demonstrate that the proposed method achieves 93.94% mAP, while reducing model parameters by 56% and computational complexity by 36% compared to YOLOv12s, effectively balancing performance and lightweight requirements.

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Mr. Zengyuan Guo

Professor Wei Xu

Professor Pingping Huang

Prof. Weixian Tan

Dr. Zhiqi Gao